package com.atguigu.day05;

import com.atguigu.bean.WaterSensor;
import org.apache.flink.api.common.eventtime.SerializableTimestampAssigner;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.functions.AggregateFunction;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.common.functions.ReduceFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.EventTimeSessionWindows;
import org.apache.flink.streaming.api.windowing.assigners.TumblingProcessingTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.time.Duration;

public class Flink03_TimeWindow_TumblingWindow_AggFun {
    public static void main(String[] args) throws Exception {
        //1.获取流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //2.从端口读取数据
        DataStreamSource<String> streamSource = env.socketTextStream("localhost", 9999);

        //3.将数据转为WaterSensor
        SingleOutputStreamOperator<WaterSensor> waterSensorStream = streamSource.map(new MapFunction<String, WaterSensor>() {
            @Override
            public WaterSensor map(String value) throws Exception {
                String[] split = value.split(",");
                return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
            }
        })
                .assignTimestampsAndWatermarks(WatermarkStrategy.<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
                .withTimestampAssigner(new SerializableTimestampAssigner<WaterSensor>() {
                    @Override
                    public long extractTimestamp(WaterSensor element, long recordTimestamp) {
                        return element.getTs()*1000;
                    }
                })
                )

                ;

        //4.将相同id的数据聚合到一块
        KeyedStream<WaterSensor, Tuple> keyedStream = waterSensorStream.keyBy("id");

        //5.开启一个基于处理时间的滚动窗口，窗口大小为5s
//        WindowedStream<WaterSensor, Tuple, TimeWindow> window = keyedStream.window(TumblingProcessingTimeWindows.of(Time.seconds(5)));
        WindowedStream<WaterSensor, Tuple, TimeWindow> window = keyedStream.window(EventTimeSessionWindows.withGap(Time.seconds(3)));

        //TODO 使用增量聚合函数 AggFun求vc和
        SingleOutputStreamOperator<String> result = window.aggregate(new AggregateFunction<WaterSensor, Integer, String>() {
            //创建累加器 初始化累加器
            @Override
            public Integer createAccumulator() {
                System.out.println("初始化累加器");
                return 0;
            }

            //累加操作
            @Override
            public Integer add(WaterSensor value, Integer accumulator) {
                System.out.println("累加操作");
                return value.getVc() + accumulator;
            }

            //获取结果
            @Override
            public String getResult(Integer accumulator) {
                System.out.println("获取结果");
                return "最终结果：" + accumulator;
            }

            //合并累加器   只在会话窗口中会调用（基于事件时间的会话窗口并且是乱序数据的情况下才会调用）
            @Override
            public Integer merge(Integer a, Integer b) {
                System.out.println("合并累加器");
                return a + b;
            }
        });

        result.print();


        env.execute();
    }
}
